This invention relates generally to information processing techniques used in the treatment of epilepsy and to devices for using these techniques.
The current state-of-the-art in workstations for processing EEG signals allow for the viewing of either monopole or bipolar montages of electrode inputs. A bipolar EEG signal represents the voltage difference between two spatially separated electrodes. Existing workstations generally do not have the capability to process and display signals produced by summing two or more monopole or bipolar EEG signals. Epileptiform activity detection software, such as that by Gotman, processes individual electroencephalogram (hereinafter xe2x80x9cEEGxe2x80x9d) channels rather than a pre-processed aggregation of selected EEG channels. In U.S. Pat. No. 6,016,449, Fischell et. al. describe an implantable system for the processing of EEG signals. Fischell et al. further describe the use of a physician""s workstation for programming a separate implantable device. Physician""s Workstations may also be used independently for patient diagnosis, treatment evaluation, and pre-implantation patient testing. Although an implantable device for detecting and stopping a neurological event, such as those described in the Fischell et al. patent, may be the final patient treatment, it is highly desirable first to determine the appropriate modality of treatment and to evaluate its potential for working with an external system. It is also highly desirable that the epileptiform activity algorithms created during patient testing and evaluation then be programmable into the implantable electrical stimulation therapy device itself.
In U.S. Pat. No. 5,311,876, Olsen et al. describe detection of seizures in a patient-independent manner by use of standardization techniques. Olsen et al. do not disclose patient-specific detection customization as part of a treatment based on electrical stimulation. Systems such as those described by Olsen et al. typically are used by neurologists to accelerate the analysis of patient EEGs by identifying spikes and other abnormal EEG waveforms.
This invention is a processed display channel based system for epileptiform activity detection which is applicable both to an implantable electrical stimulation therapy device (a neuropacemaker) and to a Physician""s Workstation System (PWS) used for pre-implant patient testing. The PWS, often based on a Windows PC, provides a neurologist with diagnostic tools for collecting and processing EEG signals and evaluating detection algorithms including patient specific parameters for detecting and stopping neurological events such as epileptic seizures, Parkinson""s tremors, and migraines. The PWS may also be used to program a neuropacemaker with the patient specific parameters for detecting and stopping neurological events identified with the PWS.
It is understood that xe2x80x9cEEGxe2x80x9d is used throughout the following discussions to encompass not only electroencephlagram data from scalp (surface) electrodes, but also electrocortigram data from intracranial electrodes. The phrase xe2x80x9cbrain electrodesxe2x80x9d is used throughout the following discussions to mean any electrodes within or near the brain including scalp (surface) electrodes and intracranial electrodes. The phrase xe2x80x9cepileptiform activityxe2x80x9d refers to activity within the brain of a person with epilepsy which is indicative of the disease. Epileptiform activity is present during a clinical epileptic seizure but may also sometimes occur without clinical symptoms.
In J. Clin. Neurophysiol, Vol 16, No. 2, p. 131, 1999; Gotman defines behavioral and electrographic seizures:
xe2x80x9cA behavioral seizure is defined as the behavioral manifestations of an epileptic seizure as perceived by the patient, seen by an observer, or recorded on videotape.xe2x80x9d
An electrographic seizure (or EEG seizure) is defined as xe2x80x9can abnormal paroxysmal EEG pattern.xe2x80x9d
For the purposes of this application, clinical seizures include behavioral seizures as defined by Gotman as well as electrographic seizures having functional or cognitive deficits that may be identified through patient testing.
The term xe2x80x9consetxe2x80x9d means the point in time at which a seizure (electrographic, behavioral or clinical) begins.
For the purposes of this application, the phrase xe2x80x9cprecursor to a seizurexe2x80x9d and term xe2x80x9cprecursorxe2x80x9d are defined as any one or more of the following:
1. A segment of EEG or of processed EEG signal prior to the onset (start) of an electrographic seizure,
2. A segment of EEG of processed EEG signal occurring at the start of an electrographic seizure,
3. A segment of EEG or processed EEG signal prior to the onset (start) of a clinical seizure.
For the purposes of this application, the use of the term xe2x80x9cseizurexe2x80x9d pertains primarily to electrographic seizures, as the processed display channel (hereinafter xe2x80x9cPDCxe2x80x9d) based neurological event detection described herein is performed on EEG or processed EEG signals.
A xe2x80x9ctemplatexe2x80x9d is defined to be any one of the following:
1. A set of algorithm detection parameters which have been programmed into a PWS or neuropacemaker.
2. The set of patient specific detection system selections used for epileptiform activity detection including PDC selection, choice of detection algorithm(s) to be used on the PDC(s), and the specific set of algorithm detection parameters which have been programmed into a PWS or neuropacemaker.
Included in this invention is the simultaneous detection by two or more detection algorithms on a single PDC signal or by two or more different detection algorithms on two or more PDC""s. For example, a DC shift algorithm may be used to process a first PDC, the first PDC being created by the addition of the two or three EEG channels which best show a DC shift prior to the onset of a clinical seizure. A waveform detector may simultaneously monitor a second PDC for a 10 to 14 Hz repetitive spikey waveform. The PDC may be created by subtracting one EEG channel from another and using bandpass filtering the difference between 5 and 20 Hz.
Preferably, the PDC based seizure detector uses two PDC""s (or one PDC with multiple parameter sets): a first PDC being optimized to depict or detect a precursor to a patients"" seizure, and a second PDC being optimized to depict or detect epileptiform activity several seconds into to seizure. Different detection algorithms, or a single algorithm with two different sets of parameters, may be programmed to detect the precursor from the first PDC and the in-seizure epileptiform activity several seconds into the seizure from the second PDC of the first PDC. This two-PDC technique has several advantages for a responsive electrical stimulation therapy device, such as:
1. If a precursor is missed, the in-seizure epileptiform activity is still detected and stimulation applied to stop the seizure.
2. If the precursor is detected and the applied stimulation is ineffective in stopping the seizure, the in-seizure epileptiform activity would be detected and a second stimulation with the same (or, perhaps, an increased) current can be applied.
3. If there is a xe2x80x9cfalse positivexe2x80x9d precursor detection where a stimulation is applied and the stimulation induces epileptiform activity, the second PDC and its in-seizure detector would detect the induced epileptiform activity and cause stimulation to be applied to stop the induced epileptiform activity.
4. In patients with very few seizures, a neuropacemaker may be implanted with only the in-seizure PDC implemented. When seizures occur and are detected, the EEG data for a period of time before the detection would be stored by the neuropacemaker. The recorded data would be used to improve the seizure detection program through identification of the seizure precursor allowing selection of first of the two PDC""s. Such data recording is disclosed by Fischell in U.S. Pat. No. 6,016,449.
5. Another option for patients with very few seizures is to have epileptiform activity induced by stimulation during pre-implant testing using the PWS. During such testing, the in-seizure detector would be optimized to detect the induced epileptiform activity. The neuropacemaker would then be implanted with the stimulator enabled with the in-seizure detector programmed with the optimized parameters established during the pre-implant testing. This type of calibration is discussed by Fischell and Morel in U.S. Patent application Ser. No. 09/323,407, the entirety of which is incorporated by reference.
The PDC or PDC""s may be configured to detect or exhibit features which resemble, but are not, epileptiform activity, such as xe2x80x9csleep spindlesxe2x80x9d. If such is the case, the present invention may be used in the following ways:
1. If the false activity is found in an EEG channel or combination of EEG channels which do not exhibit the early presence of epileptiform activity, then a first PDC would be created which shows both the early onset of epileptiform activity and the false activity. A second PDC would be created which shows only the false activity. A set of logical operations shown below describe the determination or declaration of a valid detection in the event appropriate detections occur in the first PDC and not in the second PDC.
2. The specificity of detecting an individual""s epileptiform activity is improved by determining whether an EEG is more like previously characterized epileptiform activity, or more like other neurological events such as sleep spindles within the same patient which may mimic the signals associated with epileptiform activity.
In one inventive variation, the EEG from each PDC is decomposed into the features used by the neurological event detection algorithms by a pre-processor. The features from each PDC may then be compared against a number of parameter sets (templates) from each algorithm wherein each parameter set is adjusted to detect different neurological events (e.g. onset of epileptiform activity, in-seizure epileptiform activity, sleep spindles, gamma waves, etc.). The software in the implanted device may then analyze an EEG segment from the PDC""s and evaluate its similarity to a variety of neurological events. For example, an exemplified EEG segment may satisfy the detection requirements both for epileptiform activity and for sleep spindles if the two events have similar morphologies within an individual patient. In such an instance, it is desirable to determine which template is a closer fit for the EEG segment. Such a discrimination may be performed in a variety of mathematical ways: e.g., by weighting the importance of each parameter in the algorithm and then summing the differences between the measured EEG characteristics and the corresponding template parameters for epileptiform activity and sleep spindles. The smaller sum is used to select the appropriate corresponding template and hence the diagnosis for the EEG segment.
This example used sleep spindles as an event which might cause a false detection, but any confounding EEG that is similar in morphology to epileptiform activity is suitable for improved discrimination by this technique. In some cases, an initial template match might not discriminate between epileptiform activity and a sleep spindle (or other EEG segment similar to epileptiform activity). If analysis of the EEG segment shows that the EEG segment is essentially equally similar to both templates, the invention herein will provide the option to delay detection for a period of time (typically 300 to 1500 milliseconds). In so doing the temporal progression of the EEG activity is monitored to improve detection specificity. This additional spatio-temporal aspect of the detection algorithm takes advantage of the observation that epileptiform activity spreads to different electrodes at a different rate and with a different progression than does other neurological events in the same patient.
The present invention includes many different algorithms for processing PDC""s to detect neurological events, such as Amplitude Duration, DC Shift, Waveform, Zero Crossing, Quiescent Period, and Gotman detectors. This short list of detection algorithms represents only a subset of detection algorithms suitable for use in neurological signal processing applied to PDC""s. Indeed, combinations of such algorithms may be tailored to provide specific PDC results, in various orders (permutations). Thus a very large number of possible processing algorithms are possible.
The xe2x80x9camplitude durationxe2x80x9d detector compares the average Root Mean Squared (hereinafter xe2x80x9cRMSxe2x80x9d) amplitude xe2x80x9cAxe2x80x9d in a PDC over a time duration xe2x80x9cDxe2x80x9d to a threshold xe2x80x9cTxe2x80x9d.
The xe2x80x9cDC shiftxe2x80x9d detector looks for a shift of the DC component of the PDC signal by a given amount of amplitude for a specified time, thus requiring two detection parameters: amplitude (V) and minimum time (Tmin). An alternate embodiment of the DC shift detector uses a xe2x80x9cnot to exceed thresholdxe2x80x9d Vn that would define a more limited DC range that must be between V and Vn for the minimum time Tmin to indicate a neurological event.
The xe2x80x9cwaveformxe2x80x9d detector breaks the PDC signal into half wave units. The waveform detector then counts the number of half waves of at least a minimum duration with a minimum amplitude for a specified period of time and compares this number against a preset threshold, thus requiring four detection parameters for the waveform detector algorithm. The four detection parameters are half wave minimum amplitude (Amin), half wave minimum duration (Wmin), number of half waves (N) and specified period of time (P).
The xe2x80x9czero crossingxe2x80x9d detector compares the number of zero crossings of the PDC signal with maximum and minimum allowed number of crossings Cmax and Cmin over the time period PZ.
The xe2x80x9cquiescent periodxe2x80x9d detector looks for a reduction in the PDC""s average RMS signal level below a threshold T over a time duration D.
xe2x80x9cGotmanxe2x80x9d epileptiform activity detection algorithms that have been published extensively in the literature [may also be used with PDC""s to improve reliability.
Desirably, this invention desirably provides a neurological event detector by creating one or more Processed Display Channels (PDC""s), where each PDC is a patient-specific programmable selection or combination of EEG signal channels, and then processing at least one of the PDC""s with at least one neurological event detection algorithm.
Desirably, this invention provides a neurological event detector by creating one or more Processed Display Channels (PDC""s), where each PDC is a patient-specific programmable selection or combination of EEG signal channels, and then processing two or more of the PDC""s with at least one neurological event detection algorithm.
This invention provides a neurological event detector by creating one or more Processed Display Channels (PDC""s), where each PDC is a patient-specific programmable selection or combination of EEG signal channels, and then processing at least one of the PDC""s with two or more neurological event detection algorithms.
This invention preferably implements a PDC-based seizure detector within a physician""s workstation to provide diagnosis, template creation, and testing for optimizing seizure detection procedures prior to implantation of an implantable electrical stimulation therapy device.
This invention includes the implementation of a PDC-based seizure detector within an implantable device such as a neuropacemaker.
Each PDC may be produced using, e.g., low, high, or bandpass filtering.
The present invention may utilize a PDC-programmable combination of EEG signal channels by the addition and/or subtraction of two or more EEG signals with either uniform or non-uniform weighting.
Algorithms used in the present invention may include:
a.) an amplitude duration detection algorithm for neurological event detection by processing of one or more PDC""s,
b.) a quiescent period neurological event detection algorithm for neurological event detection which looks for a reduced amplitude over a given period followed by increased activity by processing of one or more PDC""s,
c.) a waveform detector algorithm for neurological event detection by processing of one or more PDC""s,
d.) a zero crossing detector algorithm for neurological event detection by processing of one or more PDC""s, and
e.) a DC shift detector algorithm for neurological event detection by processing of one or more PDC""s.
PDC-based neurological event detection algorithms implemented in a physician""s workstation desirably are downloadable into an implantable neuropacemaker after the physician""s workstation has been used to customize the detection algorithms best to detect epileptiform activity from a specific patient.
The present invention generally uses at least two PDC""s and most desirably requires that a valid seizure detection be detected in all PDC""s.
In another variation, the inventive procedures and devices include at least two PDC""s and require that a valid seizure detection be detected in one PDC without a simultaneous or nearly simultaneous detection in a second PDC.
The PDC""s and the algorithms and parameters associated therewith may be used to detect confounding non-seizure activity, such as sleep spindles, to improve epileptiform signal identification specificity.
These and other objects and advantages of this invention will become apparent to a person of ordinary skill in this art upon careful reading of the detailed description of this invention including the drawings as presented herein.